In a nutshell, ResearchKit makes it easier for researchers to create iOS apps for their own research, focusing on three key things: consent, surveys, and active tasks. ResearchKit provides communication and instruction for the study, in addition to pre-built templates for surveys that can be used to collect Patient Reported Outcomes. Plus, ResearchKit can collect sensor data (objective patient activated outcomes) on fitness, voice, steps, and more, all working seamlessly within Apple’s HealthKit API, too, which many users have on their devices already. This allows researchers to access relevant health and fitness data (passive patient outcomes).

Five months after its launch, I’d say, in no exaggerated terms, that ResearchKit has proven to be game-changing for researchers, leapfrogging patient reported outcome studies into a “mobile first” world. However, the current framework certainly doesn’t cover the full gamut of what is needed to build a patient-centered, engaging, scaleable digital outcomes solution. If you’re planning piloting a solution around ResearchKit, here’s what you need to know:

ResearchKit offers up important benefits for medical researchers, especially when it comes to recruitment capability and the speed at which researchers can acquire insightful data to speed medical progress.

The MyHeart Counts app has been arguably the most successful example of ResearchKit use to date — it’s a great example of the recruitment capabilities provided by ResearchKit. In just 24 hours, the researchers from MyHeart Counts were able to enroll more than 10,000 patients in the study. Then they clocked an unprecedented 41,000 consented participants in less than six months (even before entering UK and Hong Kong markets). As most researchers know, recruitment can be one of the biggest challenges in building a study. But with ResearchKit, scientists are able to grow their number of participants into the thousands very quickly; it would have taken the MyHeart Counts researchers a year and 50 medical centers around the country to get to 10,000 participants.

Additionally, ResearchKit also increases the speed at which researchers are able to find the insights they’re looking for. This is mostly because people use their mobile devices constantly (most Americans clock more than two hours per day), which means that the accumulation of mass amounts of subjective (surveys), objective (sensors/active tasks) and passive (background) data happens quickly. The Asthma Health app is a great example of this, as it combines data from a phone’s GPS with information about a city’s air quality and a patient’s outcomes data, all to help patients adhere to their treatment plans and avoid asthma triggers — study participants told researchers that the app was also helping them better understand and manage their condition. The app is also assisting providers in making personalized asthma-care recommendations.